Petunia inflata possesses S-RNase-based self-incompatibility (SI), which prevents inbreeding and promotes outcrossing. Two polymorphic genes at the S-locus, S-RNase and P. inflata S-locus F-box (Pi SLF), determine the pistil and pollen specificity, respectively. To understand how the interactions between Pi SLF and S-RNase result in SI responses, we identified four Pi SLF-like (Pi SLFL) genes and used them, along with two previously identified Pi SLFLs, for comparative studies with Pi SLF 2 . We examined the in vivo functions of three of these Pi SLFLs and found that none functions in SI. These three Pi SLFLs and two other Pi SLFs either failed to interact with S 3 -RNase (a non-self S-RNase for all of them) or interacted much more weakly than did Pi SLF 2 in vitro. We divided Pi SLF 2 into FD1 (for Functional Domain1), FD2, and FD3, each containing one of the Pi SLF-specific regions, and used truncated Pi SLF 2 , chimeric proteins between Pi SLF 2 and one of the Pi SLFLs that did not interact with S 3 -RNase, and chimeric proteins between Pi SLF 1 and Pi SLF 2 to address the biochemical roles of these three domains. The results suggest that FD2, conserved among three allelic variants of Pi SLF, plays a major role in the strong interaction with S-RNase; additionally, FD1 and FD3 (each containing one of the two variable regions of Pi SLF) together negatively modulate this interaction, with a greater effect on interactions with self S-RNase than with non-self S-RNases. A model for how an allelic product of Pi SLF determines the fate of its self and non-self S-RNases in the pollen tube is presented.

Prenatal exposure to famine remarkably increases hyperglycemia risk in 2 consecutive generations of Chinese adults independent of known T2D risk factors, which supports the notion that prenatal nutrition plays an important role in the development of T2D across consecutive generations of Chinese adults. This trial was registered at www.chictr.org.cn as ChiCTR-ECH-13003644.

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Background: Although overall 5-year survival rates for ovarian cancer are poor (10-30%), stage I/IIa patients have a 95% 5-year survival. New biomarkers that improve the diagnostic performance of existing tumor markers are critically needed. A previous study by Zhang et al. reported identification and validation of three biomarkers using proteomic profiling that together improved early-stage ovarian cancer detection. Methods: To evaluate these markers in an independent study population, postdiagnostic/pretreatment serum samples were collected from women hospitalized at the Mayo Clinic from 1980 to 1989 as part of the National Cancer Institute Immunodiagnostic Serum Bank. Sera from 42 women with ovarian cancer, 65 with benign tumors, and 76 with digestive diseases were included in this study. Levels of various posttranslationally forms of transthyretin and apolipoprotein A1 were measured in addition to CA125.

These findings indicated that increased uric acid levels probably associated with obesity and type 2 diabetes, and more definite research is needed to define any role for uric acid in relation to these diseases.

In the social, behavioral, and economic sciences, it is an important problem to predict which individual opinions will eventually dominate in a large population, if there will be a consensus, and how long it takes a consensus to form. This idea has been studied heavily both in physics and in other disciplines, and the answer depends strongly on both the model for opinions and for the network structure on which the opinions evolve. One model that was created to study consensus formation quantitatively is the Deffuant model, in which the opinion distribution of a population evolves via sequential random pairwise encounters. To consider the heterogeneity of interactions in a population due to social influence, we study the Deffuant model on various network structures (deterministic synthetic networks, random synthetic networks, and social networks constructed from Facebook data) using several interaction mechanisms. We numerically simulate the Deffuant model and conduct regression analyses to investigate the dependence of the convergence time to equilibrium on parameters, including a confidence bound for opinion updates, the number of participating entities, and their willingness to compromise. We find that network structure and parameter values both have an effect on the convergence time, and for some network topologies, the convergence time undergoes a transition at a critical value of the confidence bound. We discuss the number of opinion groups that form at equilibrium in terms of a confidence-bound threshold for a transition from consensus to multiple-opinion equilibria.

The protein-degradation model invokes specific degradation of non-self S-RNases in the pollen tube mediated by an SLF, and can explain compatible versus incompatible pollination and the phenomenon of competitive interaction, where SI breaks down in pollen carrying two different S-alleles. In Solanaceae, Plantaginaceae and subfamily Maloideae of Rosaceae, there also exist multiple S-locus-linked SLF/SFB-like genes that potentially function as the pollen S-gene. To date, only three such genes, all in P. inflata, have been examined, and they do not function as the pollen S-gene in the S-genotype backgrounds tested. Interestingly, subfamily Prunoideae of Rosaceae appears to possess only a single SLF/SFB gene, and competitive interaction, observed in Solanaceae, Plantaginaceae and subfamily Maloideae, has not been observed. Thus, although the cytotoxic function of S-RNase is an integral part of SI in Solanaceae, Plantaginaceae and Rosaceae, the function of SLF/SFB may have diverged. This highlights the complexity of the S-RNase-based SI mechanism. The review concludes by discussing some key experiments that will further advance our understanding of this self/non-self discrimination mechanism.

It is believed by the majority today that the efficient market hypothesis is imperfect because of market irrationality. Using the physical concepts and mathematical structures of quantum mechanics, we construct an econophysics framework for the stock market, based on which we analogously map massive numbers of single stocks into a reservoir consisting of many quantum harmonic oscillators and their stock index into a typical quantum open system-a quantum Brownian particle. In particular, the irrationality of stock transactions is quantitatively considered as the Planck constant within Heisenberg's uncertainty relationship of quantum mechanics in an analogous manner. We analyze real stock data of Shanghai Stock Exchange of China and investigate fat-tail phenomena and non-Markovian behaviors of the stock index with the assistance of the quantum Brownian motion model, thereby interpreting and studying the limitations of the classical Brownian motion model for the efficient market hypothesis from a new perspective of quantum open system dynamics.